import tensorflow as tf
import numpy as np

def add_layer(inputs, in_size,out_size,activation_function=None):
    Weights=tf.Variable(tf.random_uniform([in_size,out_size]))
    biases=tf.Variable(tf.zeros([1,out_size])+0.1)
    Wx_plus_biases=tf.matmul(inputs,Weights)+biases
    if activation_function==None:
        outputs=Wx_plus_biases
    else:
        outputs=activation_function(Wx_plus_biases)
    return outputs

x_data=np.linspace(-1,1,300)[:,np.newaxis]
noise=np.random.normal(0,0.05,x_data.shape)

y_data=np.square(x_data)-0.5+noise

xs=tf.placeholder(tf.float32,[None,1])
ys=tf.placeholder(tf.float32,[None,1])


l1=add_layer(xs,1,10,activation_function=tf.nn.relu)
prediction=add_layer(l1,10,1,activation_function=None)

loss=tf.reduce_mean(tf.square(ys-prediction))
train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)
init=tf.initialize_all_variables()

sess=tf.Session()
sess.run(init)
for i in range(1000):
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i%50 == 0:
        #print(sess.run(tf.reduce_mean(tf.square(ys-prediction)),feed_dict={xs:x_data,ys:y_data}))
        print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))